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Identification of practitioners at high risk of complaints to health profession regulators

BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, co...

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Autores principales: Spittal, Matthew J., Bismark, Marie M., Studdert, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567559/
https://www.ncbi.nlm.nih.gov/pubmed/31196074
http://dx.doi.org/10.1186/s12913-019-4214-y
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author Spittal, Matthew J.
Bismark, Marie M.
Studdert, David M.
author_facet Spittal, Matthew J.
Bismark, Marie M.
Studdert, David M.
author_sort Spittal, Matthew J.
collection PubMed
description BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, conduct or performance issues. METHODS: Using 2011—2016 data from the national regulator of health practitioners in Australia, we conducted a retrospective cohort study of 14 registered health professions. We used recurrent-event survival analysis to estimate the risk of a complaint and used the results of this analysis to develop an algorithm for identifying practitioners at high risk of complaints. We evaluated the algorithm’s discrimination, calibration and predictive properties. RESULTS: Participants were 715,415 registered health practitioners (55% nurses, 15% doctors, 6% midwives, 5% psychologists, 4% pharmacists, 15% other). The algorithm, PRONE-HP (Predicted Risk of New Event for Health Practitioners), incorporated predictors for sex, age, profession and specialty, number of prior complaints and complaint issue. Discrimination was good (C-index = 0·77, 95% CI 0·76–0·77). PRONE-HP’s score values were closely calibrated with risk of a future complaint: practitioners with a score ≤ 4 had a 1% chance of a complaint within 24 months and those with a score ≥ 35 had a higher than 85% chance. Using the 90th percentile of scores within each profession to define “high risk”, the predictive accuracy of PRONE-HP was good for doctors and dentists (PPV = 93·1% and 91·6%, respectively); moderate for chiropractors (PPV = 71·1%), psychologists (PPV = 54·9%), pharmacists (PPV = 39·9%) and podiatrists (PPV = 34·0%); and poor for other professions. CONCLUSIONS: The performance of PRONE-HP in predicting complaint risks varied substantially across professions. It showed particular promise for flagging doctors and dentists at high risk of accruing further complaints. Close review of available information on flagged practitioners may help to identify troubling patterns and imminent risks to patients.
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spelling pubmed-65675592019-06-17 Identification of practitioners at high risk of complaints to health profession regulators Spittal, Matthew J. Bismark, Marie M. Studdert, David M. BMC Health Serv Res Research Article BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, conduct or performance issues. METHODS: Using 2011—2016 data from the national regulator of health practitioners in Australia, we conducted a retrospective cohort study of 14 registered health professions. We used recurrent-event survival analysis to estimate the risk of a complaint and used the results of this analysis to develop an algorithm for identifying practitioners at high risk of complaints. We evaluated the algorithm’s discrimination, calibration and predictive properties. RESULTS: Participants were 715,415 registered health practitioners (55% nurses, 15% doctors, 6% midwives, 5% psychologists, 4% pharmacists, 15% other). The algorithm, PRONE-HP (Predicted Risk of New Event for Health Practitioners), incorporated predictors for sex, age, profession and specialty, number of prior complaints and complaint issue. Discrimination was good (C-index = 0·77, 95% CI 0·76–0·77). PRONE-HP’s score values were closely calibrated with risk of a future complaint: practitioners with a score ≤ 4 had a 1% chance of a complaint within 24 months and those with a score ≥ 35 had a higher than 85% chance. Using the 90th percentile of scores within each profession to define “high risk”, the predictive accuracy of PRONE-HP was good for doctors and dentists (PPV = 93·1% and 91·6%, respectively); moderate for chiropractors (PPV = 71·1%), psychologists (PPV = 54·9%), pharmacists (PPV = 39·9%) and podiatrists (PPV = 34·0%); and poor for other professions. CONCLUSIONS: The performance of PRONE-HP in predicting complaint risks varied substantially across professions. It showed particular promise for flagging doctors and dentists at high risk of accruing further complaints. Close review of available information on flagged practitioners may help to identify troubling patterns and imminent risks to patients. BioMed Central 2019-06-13 /pmc/articles/PMC6567559/ /pubmed/31196074 http://dx.doi.org/10.1186/s12913-019-4214-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Spittal, Matthew J.
Bismark, Marie M.
Studdert, David M.
Identification of practitioners at high risk of complaints to health profession regulators
title Identification of practitioners at high risk of complaints to health profession regulators
title_full Identification of practitioners at high risk of complaints to health profession regulators
title_fullStr Identification of practitioners at high risk of complaints to health profession regulators
title_full_unstemmed Identification of practitioners at high risk of complaints to health profession regulators
title_short Identification of practitioners at high risk of complaints to health profession regulators
title_sort identification of practitioners at high risk of complaints to health profession regulators
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567559/
https://www.ncbi.nlm.nih.gov/pubmed/31196074
http://dx.doi.org/10.1186/s12913-019-4214-y
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